Multi-Objective Optimization of Bulk Vinyl Acetate Polymerization with Branching

Mogilicharla, Anitha; Chugh, Tinkle; Majumdar, Saptarshi; Mitra, Kishalay

doi:10.1080/10426914.2013.872271

Cited by 28 publications

(10 citation statements)

References 13 publications

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“…The results are also included in the Supplementary material. We also tested K-RVEA, RVEA, ParEGO and MOEA/D-EGO on a three objective real-world polymerization problem [34]. Even though K-RVEA and RVEA have been proposed for more than three objectives, they still performed better in solution quality and computation time than the compared algorithms.…”

Section: B Performance On Dtlz Problemsmentioning

confidence: 99%

A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Chugh

Jin

Miettinen

et al. 2018

IEEE Trans. Evol. Computat.

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459

185

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Abstract-We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art surrogate-assisted evolutionary algorithms on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.

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Section: B Performance On Dtlz Problemsmentioning

confidence: 99%

A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Chugh

Jin

Miettinen

et al. 2018

IEEE Trans. Evol. Computat.

Self Cite

459

185

View full text Add to dashboard Cite

show abstract

“…Handling objectives with different latencies: In many real-world multi-objective optimization problems, objectives may have different computation times of different objectives. For instance, in [84], [85], a decision variable is also used as an objective function and the computation time for evaluating such objective functions is negligible compared to other simulation-based objective functions. Some existing and recent studies can be applied to expensive MOPs with different latencies among objective functions.…”

Section: A On-line Data-driven Optimizationmentioning

confidence: 99%

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin

Wang

Chugh

et al. 2019

IEEE Trans. Evol. Computat.

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443

144

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Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

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“…It was also compared with ParEGO, MOEA/D-EGO and SMS-EGO using IGD and hypervolume. In addition to benchmark problems, the algorithm was also tested on a free-radical polymerization problem [96] and also compared with the state-of-the-art algorithms. In the given number of function evaluations, the proposed algorithm performed better than other algorithms.…”

Section: Kriging Based Algorithmsmentioning

confidence: 99%

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

et al. 2017

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Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008-2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation based algorithms. We also compare these algorithms based on different criteria such as metamodelling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive mmultiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.

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Multi-Objective Optimization of Bulk Vinyl Acetate Polymerization with Branching

Cited by 28 publications

References 13 publications

A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

Data-Driven Evolutionary Optimization: An Overview and Case Studies

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

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